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1. What Is Reinforcement Learning?
2. OpenAI Gym
3. Deep Learning with PyTorch
4. The Cross-Entropy Method
5. Tabular Learning and the Bellman Equation
5. Deep Q-Networks
6. Higher-Level RL libraries
7. DQN Extensions
8. Ways to Speed up RL
9. Stocks Trading Using RL
10. Policy Gradients ? an Alternative
11. The Actor-Critic Method
12. Asynchronous Advantage Actor-Critic
13. Training Chatbots with RL
14. The TextWorld environment
15. Web Navigation
16. Continuous Action Space
17. RL in Robotics
18. Trust Regions ? PPO, TRPO, ACKTR, and SAC
19. Black-Box Optimization in RL
20. Advanced exploration
21. Beyond Model-Free ? Imagination
22. AlphaGo Zero
23. RL in Discrete Optimisation
24. Multi-agent RL

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks.

With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik's Cube), multi-agent methods, Microsoft's TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.

In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization.

In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.